Text Generation
Transformers
Safetensors
qwen3_moe
cybersecurity
defensive-security
merged-model
private
conversational
Instructions to use BlackwoodAI/Darkforest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BlackwoodAI/Darkforest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BlackwoodAI/Darkforest") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BlackwoodAI/Darkforest") model = AutoModelForCausalLM.from_pretrained("BlackwoodAI/Darkforest", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BlackwoodAI/Darkforest with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BlackwoodAI/Darkforest" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BlackwoodAI/Darkforest", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BlackwoodAI/Darkforest
- SGLang
How to use BlackwoodAI/Darkforest with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "BlackwoodAI/Darkforest" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BlackwoodAI/Darkforest", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "BlackwoodAI/Darkforest" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BlackwoodAI/Darkforest", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use BlackwoodAI/Darkforest with Docker Model Runner:
docker model run hf.co/BlackwoodAI/Darkforest
Darkforest
Darkforest is a private merged checkpoint for authorized defensive cybersecurity research.
Intended Use
Darkforest is intended for authorized defensive workflows only, including:
- CTF and lab analysis
- secure code review
- vulnerability explanation
- detection engineering
- exploit reproduction in owned test environments
- malware analysis at a defensive or forensic level
Restrictions
Do not use this model for unauthorized access, credential theft, malware deployment, phishing, evasion, botnets, fraud, or real-world harm.
Loading
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "BlackwoodAI/Darkforest"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
device_map="auto",
)
Serving
This checkpoint can be served with vLLM as an OpenAI-compatible endpoint.
Notes
This is a private research checkpoint. Evaluate before relying on it for any workflow.
- Downloads last month
- 31